A Novel Adaptive Fuzzy Local Information C -Means Clustering Algorithm for Remotely Sensed Imagery Classification

This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by produc...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 55; H. 9; S. 5057 - 5068
Hauptverfasser: Zhang, Hua, Wang, Qunming, Shi, Wenzhong, Hao, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Blurring
Classification
Clustering
Clustering algorithms
fuzzy c-means (FCM) clustering
Image classification
Image edge detection
Image processing
Image segmentation
Imagery
Linear programming
local measure similarity
Nickel
Noise measurement
Noise reduction
Pixels
Remote sensing
remotely sensed imagery
Robustness
Similarity
Similarity measures
spatial information
Weighting
ISSN:0196-2892, 1558-0644
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Zusammenfassung:This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2702061